Text Generation
PEFT
Safetensors
English
qlora
data-science
code-generation
qwen2
lora
sft
unsloth
conversational
Instructions to use jsmall12/DataSci-Coder-14B-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jsmall12/DataSci-Coder-14B-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-14B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "jsmall12/DataSci-Coder-14B-LoRA") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use jsmall12/DataSci-Coder-14B-LoRA with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jsmall12/DataSci-Coder-14B-LoRA to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jsmall12/DataSci-Coder-14B-LoRA to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jsmall12/DataSci-Coder-14B-LoRA to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="jsmall12/DataSci-Coder-14B-LoRA", max_seq_length=2048, )
Upload README.md with huggingface_hub
Browse files
README.md
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# DataSci-Coder-14B: Qwen2.5-Coder-14B LoRA Adapter for Data Science
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A QLoRA fine-tuned adapter for [Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) optimized for data science code generation. The model outputs clean, runnable Python code with zero explanatory text — strictly following code-only instructions.
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## Key Results
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- Generates complete, runnable Python code for data science tasks
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- Covers statistics, machine learning, deep learning, NLP, time series, and visualization
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- Follows instructions
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- Handles complex tasks: Bayesian inference, VAEs, GANs, survival analysis, stacking ensembles, SHAP, anomaly detection
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## Training Details
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# DataSci-Coder-14B: Qwen2.5-Coder-14B LoRA Adapter for Data Science
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[](https://github.com/jacksonSmall/DataSci-Coder)
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A QLoRA fine-tuned adapter for [Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) optimized for data science code generation. The model outputs clean, runnable Python code with zero explanatory text — strictly following code-only instructions.
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## Key Results
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- Generates complete, runnable Python code for data science tasks
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- Covers statistics, machine learning, deep learning, NLP, time series, and visualization
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- Follows instructions precisely — when told "no explanations," it outputs only code (base model ignores this 40% of the time)
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- Handles complex tasks: Bayesian inference, VAEs, GANs, survival analysis, stacking ensembles, SHAP, anomaly detection
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## Training Details
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